For the coarse depth estimation in view synthesis stage, We customize CoordNet [1]} with ASPP module [2]} and deeper convolution layers. From Table 3 in the main paper, we can see that our customized CoordNet have less parameters than RectNet [3]}, it outperforms RectNet in both one-stage and full two-stage pipeline omnidirectional depth estimation. The detailed network implementation is shown in Table REF , the architecture of the CoordNet as well as view synthesis is shown in Figure REF . The output coarse depth is then fed into depth image based rendering (DIBR) module together with original RGB 360 image for novel view synthesis given baseline. The DIBR implementation is based on [4]}. The pixels from source image are splatted on an empty target image, then occlusions are handled by soft z-buffering. The final prediction is the weighted average of points which splat to the same pixel.
